1. Technical Field
One or more embodiments relate generally to systems and methods for person recognition. More specifically, one or more embodiments relate to systems and methods of recognizing unknown person instances in images of an image gallery.
2. Background and Relevant Art
Digital photography and increasing digital storage sizes allow users to take large numbers of digital photographs. Images captured within certain time frames or at similar locations are typically related to each other, and often include many of the same people. Labeling or tagging people within the images, however, is frequently burdensome and time consuming. For example, manually tagging people across many images in many different albums can be a large task that deters users from tagging people in more than a few of the images.
To alleviate the burden of tagging people in images, some conventional recognition systems automatically find and recognize faces in images. Specifically, the conventional recognition systems can use automatic facial recognition to predict the identity of a face given a set of images including the face and one or more manually labeled images. For example, some conventional recognition systems automatically recognize one or more faces in an image and tag the images with metadata associated with the automatically recognized face(s). Alternatively, other conventional recognition systems recognize faces and provide a recommendation of the recognized faces to a user.
Although conventional recognition systems are able to use facial recognition to automatically predict the identity of a person using facial features, such systems can often be limited in accuracy and ability to provide a prediction. In particular, in real-world applications, developing a facial recognition system that is able to account for large interpersonal variations due to pose changes, occlusion, low image quality, etc., can be challenging. Additionally, conventional recognition systems are often unable to differentiate multiple identities when interpersonal variations of faces are subtle. Furthermore, facial recognition is typically unable to automatically recognize people in images when only part or none of a person's face is visible in an image. Thus, conventional systems that use facial recognition alone are often unable to correctly identify faces and people in many instances.
Other conventional recognition systems use other information instead of, or in addition to, facial features to recognize people in images. Specifically, some conventional recognition systems use poselet detectors and/or holistic image features to detect people in images. For example, the conventional systems use body pose information to recognize a person in multiple images based on the similar pose information. Such conventional systems improve person recognition accuracy over systems that use facial recognition alone, but are impractical due to the computational costs associated with poselet detection and poselet feature evaluation.
These and other disadvantages may exist with respect to conventional recognition techniques.